Sentence features relevance for extractive text summarization using genetic algorithms

Abstract

Preprocessing, term selection, term weighting, sentence weighting, and sentence selection are the main issues in generating extractive summaries of text sentences. Although many outstanding related works only are focused in the last step, they show sophisticated features in each one. In order to determine the relevance of the sentences (sentence selection step) many sentence features have been proposed in this task (in fact, these features are related to all the steps). Recently, some good related works have coincided in the same features but they present different ways for weighting these features. In this paper, a method to optimize the combination of previous relevant features in each step based on a genetic algorithm is presented. The proposed method not only outperforms previous related works in two standard document collections, but also shows the relevance of these features to this problem.